A receptance coupling procedure considering frequency-dependent behavior of holder-tool contact dynamics

Abstract

The current paper proposes a receptance coupling (RC) procedure considering all the frequency-dependent translational and rotational dynamics of the holder-tool contact. To this end, a novel implementation of the inverse receptance coupling (IRC) is conducted. In this implementation, the holder-tool contact, which includes tool portion inserted into the holder, is treated as a flexible component that is connected to the holder, and all its translational and rotational receptances are analytically identified without any assumption, unlike the existing studies in the literature. The holder-tool contact dynamics identification is completed by using two types holders such as shrink fit and collet chuck, and a carbide tool blank inserted into these holders. This procedure is carried out for four different tool blank contact/insertion lengths for each holder. To validate the proposed procedure, the identified holder-tool contact receptances are then employed in the spindle-holder-endmill assembly or tool point displacement-to-force receptance predictions with the RC method for all the corresponding holder type-contact length combinations. Those RC predictions are also completed with the holder-contact receptances achieved via an existing method. In addition, the above-mentioned tool point displacement-to-force receptances are experimentally acquired. Afterwards, for a reliable and an efficient assessment of the proposed procedure, all the predicted and measured tool point displacement-to-force receptances are both graphically and numerically compared to each other by using a frequency-dependent magnitude similarity metric. The assessment results show that, based on the high similarity metric values, the proposed holder-tool contact identification procedure ensures a well agreement between the predictions and the measurements. Besides, both the proposed procedure and the existing method enable highly similar tool point displacement-to-force predictions.